AN INTEGRATED FRAMEWORK OF BARRIERS AND CRITICAL SUCCESS FACTORS FOR EFFECTIVE LEAN SIX SIGMA ADOPTION IN SMES

Abstract

The implementation of Lean Six Sigma (LSS) in small and medium-sized enterprises (SMEs) has enormous potential improvements on processes, reduction of wastes, and future efficiency but this is usually held back due to resistance to change, expertise lacks and unavailability of resources. The study forms a composite framework to integrate barriers and critical success factors (CSFs) to support the integration of LSS during the process of adoption into SMEs. “Four machine learning algorithms; Decision Tree, Random Forest, Support Vector Machine (SVM), and K-Nearest Neighbors (KNN) were applied to a dataset of 120 manufacturing, service, and technology SMEs”. It has been shown that Wind Random Forest has the best predictive accuracy (91%), precision (0.90), recall (0.91), and AUC (0.93) compared to other models. The top management support (0.30) and employee engagement (0.27) were also defined as the most influential CSFs, whereas the strongest barriers were resistance to change (0.35) and lack of expertise (0.30) according to the principle of feature importance analysis. The combined analysis proves that the effects of barriers could be reduced by the effects of CSFs provided that SMEs can implement LSS successfully despite the lack of resources. The suggested framework will provide entrepreneurs with a systematic roadmap on how they can give interventions the importance that it deserves, improve the working engagement, and develop a steady culture of continuous improvement. The research will be informative in developing actionable knowledge to bring the gap between theory and practice of LSS in SMEs.